Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add oyi77/1ai-skills --skill telegram-userbotgit clone --depth 1 https://github.com/oyi77/1ai-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/oyi77/1ai-skills/telegram-userbot)<a href="https://agentmods.dev/skills/oyi77/1ai-skills/telegram-userbot"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/telegram-userbot/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/oyi77/1ai-skills/telegram-userbot"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/telegram-userbot.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00070 | $0.00685 |
| Opus 5 | $0.00035 | $0.00342 |
| Sonnet 5 | $0.00014 | $0.00137 |
| Haiku 4.5 | $0.00007 | $0.00068 |
Grade A, and why
telegram-userbot scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Telegram Userbot
When to Use
Trigger phrases:
-
"telegram userbot"
-
"Use when working with telegram userbot"
-
Automating Telegram messaging (DMs, group messages)
-
Scraping Telegram group/channel members
-
Running Telegram-based lead generation
-
Automating Telegram account activities
-
Building Telegram automation workflows
When NOT to Use
- For one-off tasks that will never repeat
- When the process requires human judgment at every step
- When the cost of automation exceeds the cost of manual execution
Overview
Telegram Userbot automates workflow automation to reduce manual effort and increase reliability.
Workflow
# Example: Workflow automation
import schedule
import time
def run_workflow():
data = fetch_data()
processed = transform(data)
deliver(processed)
schedule.every().hour.do(run_workflow)
while True:
schedule.run_pending()
time.sleep(60)
- Define triggers — Set up events or schedules that initiate the automation
- Configure inputs — Specify data sources and parameters
- Design pipeline — Define the sequence of automated steps
- Add error handling — Set up retries, alerts, and fallback paths
- Test end-to-end — Validate the full automation with realistic data
- Deploy and monitor — Activate and track performance
Configuration
- Set trigger conditions (schedule, webhook, event)
- Define input validation rules
- Configure notification channels for alerts
- Set retry policies and timeout limits
Best Practices
- Start with simple automations and iterate
- Add logging at every step for debugging
- Use idempotent operations where possible
- Test with edge cases before deploying
Anti-Rationalization Table
| Rationalization | Reality |
|---|---|
| "Manual is faster for one-off tasks" | One-off tasks become recurring. Automate early, save time later. |
| "I will add error handling later" | You never do. Handle errors from day one. |
| "Automation is overkill" | If you do it twice, automate it. If you do it daily, it is critical infrastructure. |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 110 lines · 70 tokens per session scan A c9c0ebc6603a
telegram-userbot is a skill published in the GitHub repository oyi77/1ai-skills (12 stars, last pushed today), licensed MIT. It adds 70 tokens to every session and 685 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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